10 ± 3.11 and post 3.80 ± 3.18). Fewer total blood products were administered in the postpathway group (pre 1.5 ± 1.8 and post 0.8 ± 1.5). Discussion The maintenance of clinical outcomes in the postpathway cohort, while having a greater CCI, indicates the same quality of care was provided for a more medically complex patient population. With a decrease in total blood products in the postpathway group, this highlights the economic importance of perioperative optimization that can be obtained in a multidisciplinary pathway. Conclusion Implementation of a multidisciplinary hip fracture pathway is an effective strategy for maintaining care standards for fragility hip fracture management, particularly in the setting of complex medical comorbidities.Objectives To layout mHealth (mobile health) applications operating in India with the facility of either online doctor consultation or offline doctor appointment booking. Methods A cross-sectional, observational and web-based study was conducted. We searched the Google Play Store with the search strategy "health apps in India". In the results, 250 applications (apps) appeared. Out of 250 apps, finally, 22 apps were found to be providing online doctor consultation and/or doctor appointment booking-related services. Results Among the selected mHealth apps operating in India and providing doctor consultation-related services online/offline, Practo, mfine, DocsApp, 1mg, Netmeds, Lybrate, MediBuddy, and Medlife were found to be the eight most popular ones with over a million downloads and on average four-plus user rating out of five. Practo, mfine, and Lybrate offer doctor consultation through chat, audio, and video calling. Netmeds and DocsApp offer doctor consultation through both chat and audio call. 1mg offers free chat consultation, while MediBuddy and Medlife offer audio call consultation only. Considering booking doctor appointments for offline consultation, Practo, mfine, 1mg, and Lybrate only offer this facility among the eight most popular selected mHealth apps. Conclusions mHealth apps providing doctor consultation are gaining popularity in India, and they have enormous potential in the country. The government should make enabling policies to facilitate and popularise mHealth apps.Objectives Monitoring healthcare activities is the first step for health stakeholders and health professionals to improve the quality and performance of healthcare services. However, monitoring remains a challenge for healthcare facilities, especially in developing countries. Fortunately, advances in business analytics address this need. This paper aims to describe the experience of a low-income healthcare facility in a developing country in using business analytics descriptive techniques and to discuss business analytics implementation challenges and opportunities in such an environment. Methods Business analytics descriptive techniques were applied on 3 years' electronic medical records of outpatient consultation of the University Psychiatric Centre (CPU) of Casablanca. Statistical analysis was conducted to compare results over years. https://www.selleckchem.com/products/perhexiline-maleate.html Results Over the 3 monitored years, the monthly number of computerized physician order entries increased significantly (p less then 0.001). Physicians improved their personal recording over years. Schizophrenia as well as depressive and bipolar disorders were noted at the top of outpatient mental disorders. Antipsychotics are the most prescribed drugs, and a significant annual decrease in outpatient care wait time was noted (p less then 0.001). Conclusions Business analytics allowed CPU to monitor mental healthcare outpatient activity and to adopt its business processes according to outcomes. However, challenges mainly in the organizational dimension of the decision-making process and the definition of strategic key metrics, data structuration, and the quality of data entry had to be considered for the optimal use of business analytics.Objectives To examine the direct effects of risk factors associated with the 5-year costs of care in persons with alcohol use disorder (AUD) and to examine whether remission decreases the costs of care. Methods Based on Electronic Health Record data collected in the North Karelia region in Finland from 2012 to 2016, we built a non-causal augmented naïve Bayesian (ANB) network model to examine the directional relationship between 16 risk factors and the costs of care for a random cohort of 363 AUD patients. Jouffe's proprietary likelihood matching algorithm and van der Weele's disjunctive confounder criteria (DCC) were used to calculate the direct effects of the variables, and sensitivity analysis with tornado diagrams and analysis maximizing/minimizing the total cost of care were conducted. Results The highest direct effect on the total cost of care was observed for a number of chronic conditions, indicating on average more than a ?26,000 increase in the 5-year mean cost for individuals with multiple ICD-10 diagnoses compared to individuals with less than two chronic conditions. Remission had a decreasing effect on the total cost accumulation during the 5-year follow-up period; the percentage of the lowest cost quartile (42.9% vs. 23.9%) increased among remitters, and that of the highest cost quartile (10.71% vs. 26.27%) decreased compared with current drinkers. Conclusions The ANB model with application of DCC identified that remission has a favorable causal effect on the total cost accumulation. A high number of chronic conditions was the main contributor to excess cost of care, indicating that comorbidity is an essential mediator of cost accumulation in AUD patients.Objectives Skills to employ nursing informatics to promote the health of individuals is of such importance that it is considered a core competence. Although investments are made to increase the use of e-health, there is no full understanding of the usability of e-health for healthcare. This paper presents a current picture of how e-health and m-health are defined and used as well as the effects their usage may have on the intended target group. Methods Peer-reviewed open-access papers and grey literature that define e-health and m-health from PubMed, SpringerLink, and Google.com were randomized. A mixed method design with an inductive approach was employed. Open-source software were used for analysis. Results The overview includes 30 definitions of e-health and m-health, respectively. The definitions were thematised into 14 narrative themes. The results of the study, and primarily a three-level model, provide an understanding of how different types of e-health and m-health can be put into practice, and the effects or consequences of using them, which may be either positive or negative.